ECG arrhythmia recognition using artificial neural network with S-transform based effective features

In this paper, a potential application of Stock-ewell transforms (S-transform) is proposed to classify the ECG beats of the MIT-BIH database arrhythmias. Feature extraction is the important component of designing the system based on pattern recognition since even the best classifier will not perform better if the good features are not chosen properly. In this study, S-transform is used to extract the eight features which are appended with four temporal features. In this work, the performances of two approaches are compared to classify the five classes of ECG beats which is recommended by AAMI EC57 1998 standard (Association for the Advancement of Medical Instrumentation). The first approach uses temporal and S-transform based feature set, whereas the second approach uses the wavelet transform based features. These features from two approaches are independently classified using feed forward neural network (NN). Performance is evaluated on several normal and abnormal ECG signals of the MIT-BIH arrhythmia database using two techniques such as temporal and S-transform with NN classifier (TST-NN) and other wavelet transform with NN classifier (WT-NN). The experimental results demonstrate that the TST-NN technique shows better performance compared to the WT-NN technique.